This document forms part of the data and code deposited at:
https://github.com/acp29/Elmasri_GRIN2B

Load package requirements

if (!require(package="tidyverse")) utils::install.packages("tidyverse")
library(tidyverse) 
if (!require(package="lme4")) utils::install.packages("lme4")
library(lme4)  
if (!require(package="HLMdiag")) utils::install.packages("HLMdiag")
library(HLMdiag) 
if (!require(package="parameters")) utils::install.packages("parameters")
## Warning: package 'parameters' was built under R version 4.1.2
library(parameters) 
if (!require(package="car")) utils::install.packages("car")
library(car)  
if (!require(package="performance")) utils::install.packages("performance")
library(performance)  
if (!require(package="BayesFactor")) utils::install.packages("BayesFactor")
library(BayesFactor) 
if (!require(package="bayestestR")) utils::install.packages("bayestestR")
library(bayestestR) 
if (!require(package="stats")) utils::install.packages("stats")
library(stats)
if (!require(package="pCalibrate")) utils::install.packages("pCalibrate")
library(pCalibrate)
if (!require(package="afex")) utils::install.packages("afex")
library(afex)
if (!require(package="emmeans")) utils::install.packages("emmeans")
library(emmeans)
if (!require(package="multcomp")) utils::install.packages("multcomp")
library(multcomp)
if (!require(package="knitr")) utils::install.packages("knitr")
library(knitr)
if (!require(package="kableExtra")) utils::install.packages("kableExtra")
library(kableExtra)
if (!require(package="ggplot2")) utils::install.packages("ggplot2")
library(ggplot2)
if (!require(package="qqplotr")) utils::install.packages("qqplotr")
library(qqplotr)
if (!require(package="gridExtra")) utils::install.packages("gridExtra")
library(gridExtra)
if (!require(package="ggforce")) utils::install.packages("ggforce")
library(ggforce)
if (!require(package="devEMF")) utils::install.packages("devEMF")
library(devEMF)
if (!require(package="effectsize")) utils::install.packages("effectsize")
library(effectsize)

Read text in from file

Data <- read.delim("../data/n2b_mutant.dat", header = TRUE)
# Explicit nesting (required for anovaBF)
Data %>% 
  mutate(mutation = as.factor(mutation)) %>% 
  mutate(animal = paste0(as.numeric(mutation),animal)) %>% 
  mutate(slice = paste0(animal,slice)) %>% 
  mutate(pair = paste0(slice,pair)) %>% 
  mutate(pair = factor(pair)) -> Data

Factor encoding

Data$mutation <- as.factor(Data$mutation)
Data$transfection <- as.factor(Data$transfection)
Data$animal <- as.factor(Data$animal)
Data$slice <- as.factor(Data$slice)
Data$pair <- as.factor(Data$pair)

Set mutation WT and transfection - as reference levels

Data$mutation <- factor(Data$mutation, levels=c("WT","R540H","R696H","C456Y","C461F"))
Data$transfection <- factor(Data$transfection, levels=c("-","+"))

lmer settings

settings <- lmerControl(check.conv.singular = .makeCC(action = "ignore",  tol = 1e-4), boundary.tol=0)

Fit a mixed linear model

# Initialize
variates <- c("peaknmda","decaynmda","dt50nmda","chargenmda","peakampa","decayampa","dt50ampa","chargeampa")

l <- length(variates)

for (i in 1:l) {
  
variates[i] -> resp  

cat('\n\n\n# Analysis of',resp,'\n\n')

# Plot data
# colours selected from:
#  > library(scales)
#  > show_col(hue_pal()(9))
p1 <- Data %>%
    mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
           grouping=interaction(pair, mutation)) %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
    ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) + 
    geom_blank() +
    geom_line(aes(mutation_jittered), alpha=0.2, color="grey") +
    geom_point(aes(mutation_jittered), alpha=0.4, shape=16) +
    scale_color_manual(values=c("grey","#00BA38")) +
    stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0,inherit.aes=FALSE) + 
    stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1, inherit.aes=FALSE) +
    ylab(resp) +
    ggtitle("a") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.title = element_blank(),
          legend.position = "top")
p2 <- Data %>% 
    pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>% 
    mutate(ratio = `+`/`-`) %>%
    ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
    geom_sina(alpha=0.9, shape = 16) + 
    scale_color_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
    stat_summary(fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0) + 
    stat_summary(fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1) +
    ylab("ratio") +
    ggtitle("b") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.position = "none")
grid.arrange(p1, p2, nrow = 1, ncol = 2, top=sprintf("Summary plots of the data for: %s\n",resp))

# Fit the model with planned contrasts and perform hypothesis testing
  
# Setup planned, orthogonal contrasts
# ("WT","R540H","R696H","C456Y","C461F")
# According to features of the mutations or experiments in heterologous expression systems
WT_vs_Mutants <- c(-4,1,1,1,1)/5
LOF_vs_GOF    <- c(0,2,2,-2,-2)/4
LOF           <- c(0,0,0,-1,1)/2
GOF           <- c(0,-1,1,0,0)/2
contr.orth <- cbind(WT_vs_Mutants, LOF_vs_GOF, LOF, GOF)
rownames(contr.orth) <- levels(Data$mutation)

# Check that contrasts are indeed orthogonal
contr.orth %>%
  cor() %>%
  knitr::kable(caption = sprintf("**All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()
contr.orth %>%
  colSums() %>%
  as.data.frame() %>%
  rename(.,'sum' = '.') %>%
  knitr::kable(caption = sprintf("**Sum of each orthogonal contrast should be zero: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Fit model (with orthogonal contrasts)
contrasts(Data$mutation) <- contr.orth
attr(Data$mutation,"contrasts") %>%
  as.data.frame() %>%
  rownames_to_column(var = "mutation") %>% 
  knitr::kable(caption = sprintf("**Matrix of contrasts on mutation: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()
contrasts(Data$transfection) <- rbind(-1,1)/2
attr(Data$transfection,"contrasts") %>% 
  as.data.frame() %>%
  rename(contrast = "V1") %>%
  rownames_to_column(var = "transfection") %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+")  %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-")  %>% 
  knitr::kable(caption = sprintf("**Matrix of contrasts on transfection: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print() 
formula <- sprintf("log(%s) ~ mutation * transfection + (1|animal/slice/pair)", resp)
model <- lme4::lmer(formula, data = Data, REML = TRUE, control = settings, na.action = "na.fail") 

# Checking model assumptions
resid = residuals(model)
n = length(resid)
stdev = sqrt((n-1)/n) * sd(resid) # standard deviation with denominator n
std_resid = resid/stdev
p1 <- ggplot(Data, aes(x = fitted(model), y = std_resid)) +
  geom_point() +
  ggtitle("a") +
  xlab("Fitted values") + ylab("Standardized Residuals") +
  geom_hline(yintercept = 0) +
  geom_quantile(formula=y~x, color="#619CFF", size=1) +
  geom_smooth(method="loess", formula = y ~ x, color="#F8766D", size=1, se=FALSE)
p2 <- ggplot(Data, aes(x = std_resid)) +
  geom_histogram(aes(y=..density..), binwidth = 0.9*n^(-1/5), fill="#619CFF", alpha=0.33)  +
  geom_density(kernel="gaussian", alpha=0, color="#619CFF", size=1) +
  ggtitle("b") +
  xlab("Standardized Residuals") + ylab("Density") +
  geom_vline(xintercept = 0) +
  geom_function(fun = dnorm, args = list(mean=0, sd=1), col = "#F8766D", size = 1)
p3 <- ggplot(Data, aes(sample = std_resid)) +
  geom_qq_band(distribution = "norm", bandType = "ts", mapping = aes(fill = "TS"), fill="#619CFF", alpha = 0.33) +
  stat_qq() + 
  stat_qq_line(color="#F8766D",size=1) +
  ggtitle("c") +
  xlab("Normal Quantiles") + ylab("Sample Quantiles") 
infl <- hlm_influence(model, level="pair:(slice:animal)")
p4 <- infl %>% 
  mutate(influential = cooksd > 1.0) %>% 
  ggplot(aes(x=`pair:(slice:animal)`,y=cooksd, color=influential)) + 
  geom_segment(aes(x=`pair:(slice:animal)`, xend=`pair:(slice:animal)`, y=0, yend=cooksd)) + 
  geom_point() + 
  ylab("Cook's distance") +
  scale_color_manual(values=c("#619CFF","#F8766D")) + 
  ggtitle("d") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "none",
        panel.background = element_rect(color="#EBEBEB"),
        panel.grid = element_blank(),
        panel.grid.minor.y = element_line(color = "white", size=0.25),
        panel.grid.major.y = element_line(color = "white", size=0.5),
        axis.line = element_blank(),
        axis.line.x = element_line(size = 0.5, colour = "black"))
grid.arrange(p1, p2, p3, p4, nrow=2, ncol=2, top=sprintf("Plots of standardized model residuals and Cook's distances: %s\n",resp))

# Calculate ANOVA table for the fitted model (Type III sum of squares) 
car::Anova(model, type = 3, test.statistic = "F") %>%       # Uses Kenward-Roger degrees of freedom
  as.data.frame() %>%
  rownames_to_column(var="Source") %>%
  filter(Source != "(Intercept)") -> aov

# Calculate Bayes Factors for ANOVA and append them to the ANOVA data frame
# Inclusion Bayes Factor based on matched models (prior odds uniform-equal)
Data %>% 
  mutate(logresp = log(!!sym(resp))) %>%
  as.data.frame() -> Data
set.seed(123456)
anovaBF(logresp ~ mutation * transfection + animal + slice + pair, 
                 whichRandom = c("animal","slice","pair"), 
                 whichModels = "withmain", 
                 iterations = 20000,
                 data = Data) %>%
  bayesfactor_inclusion(match_models = TRUE) %>% 
  as.data.frame() %>% 
  na.omit() %>%    # removes the (nuisance) random factors
  mutate(BF = exp(log_BF)) %>%
  mutate_at("BF", formatC, format='g',digits = 3) %>% 
  dplyr::select(BF) %>% 
  unlist() -> aov$BF

# Calculate orthogonal contrasts and append them to the ANOVA summary table
# I go to the trouble of transforming the t-statistic (which is returned from the linear model) 
# to an F statistic but they give identical p-values; I think this makes more sense and provides 
# more consistency when splitting the source of variation into orthogonal contrasts and presenting 
# them in an ANOVA table (eg. like with summary.aov or orthogonal contrasts in SAS)
model_parameters(model, df_method = "kenward", exponentiate = TRUE, effects = "fixed") %>%  
  filter(grepl(":",Parameter)) %>%                          # select interaction terms only
  rename(Source = Parameter) %>%                            # 
  mutate(Df = 1) %>%                                        # set numerator degrees of freedom
  add_column(BF = "")  %>%                                  # add empty column for Bayes factors
  rename(Df.res = df_error) %>%                             # set denominator degrees of freedom
  mutate(F = abs(t)^2) %>%                                  # calculate F statistic
  mutate(`Pr(>F)` = pf(F,Df,Df.res,lower.tail=FALSE)) %>%   # calculate p value
  dplyr::select(c(Source,F,Df,Df.res,`Pr(>F)`,BF)) %>%      # select columns of interest for table
  rbind(aov,.) %>%                                          # row bind with anova table
  mutate(`Pr(>F)` = afex::round_ps_apa(`Pr(>F)`)) %>%       # format p values as APA style
  knitr::kable(caption = sprintf("**ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  add_indent(c(4:7)) %>%                                    # add indentation to indicate source components
  print()

# Calculate intraclass correlation coefficients (ICC) for the random effects
icc(model, by_group=TRUE, tolerance=0) %>% 
  as.data.frame() %>% 
  mutate(N = ngrps(model)) %>%
  rbind(.,c("residual",1-sum(.$ICC),nobs(model))) %>%
  mutate(ICC = as.numeric(ICC)) %>%                               
  knitr::kable(caption = sprintf("**Intraclass correlation coefficients for random effects: %s**",resp), digits = 3) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Calculated estimated marginal means, By default, emmeans uses Kenward-Roger's method for estimating the degrees of freedom
emm <- emmeans(model, ~ mutation * transfection, data = Data, tran = 'log', type = 'response')
emm %>% 
  summary(calc = c(n = ".wgt.")) %>%
  as.data.frame() %>%
  mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+")  %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-")  %>% 
  relocate(df, .before = response) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Calculate overall average for untransfected neurons
emmeans(model, ~ mutation * transfection, data = Data) %>%
  as.data.frame() %>%
  filter(transfection == "-") %>%
  dplyr::select(emmean) %>% 
  colMeans() %>%
  exp() %>%
  sprintf("**Overall average of %s for untransfected neurons**: %.2f",resp,.) %>%
  print()

# Calculate transfected/untransfected ratios
emm.transfection <- contrast(emm, method = "trt.vs.ctrl", interaction = FALSE, by = 'mutation', adjust = "none")
emm.transfection %>%
  confint() %>%
  as.data.frame() %>%
  relocate(df, .before = ratio) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals for transfected/untransfected ratios: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# 95% confidence intervals for interaction contrasts
emm.interaction <- contrast(emm, method = "trt.vs.ctrl", interaction = TRUE, adjust = "none")
emm.interaction %>%
  confint() %>% 
  relocate(df, .before = ratio) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**95%% confidence intervals for contrasts: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Standardized effect sizes (*r*) for interaction contrasts
# Methods used the same as this server: https://easystats4u.shinyapps.io/statistic2effectsize/
emm.interaction %>% 
    as.data.frame() %>% 
    mutate(n = df+nrow(.)+1) %>% 
    mutate(r = t_to_r(t.ratio, df)$r) %>% 
    mutate(z = atanh(r),
           SE = 1/sqrt(n-3),
           CI = sprintf("[%.2f, %.2f]",
                        LL = tanh(z - 1.96*SE),
                        UL = tanh(z + 1.96*SE))) %>%
    dplyr::select(-c(ratio,SE,df,null,t.ratio,p.value,z)) %>%
    knitr::kable(col.names = c("mutation",
                               "transfection",
                               "*n*",
                               "*r*",
                               "95% *CI*"),
                 caption = sprintf("**Standardized effect sizes (*r*) for contrasts: %s**",resp), digits = 2) %>% 
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
    print()

posthoc = FALSE

if (posthoc == TRUE) {
  
# p-values and maximum Bayes Factors for interaction contrasts 
# Dunnett's step-down adjustment to control FWER on p-values (using multcomp package)
# Chapter 4.1.2 in Bretz, F., Hothorn, T. and Westfall, P. (2011) Multiple Comparisons Using R. Taylor and Frances Group, LLC.
emm.interaction %>%   
    as.glht() %>%
    summary(test = adjusted(type = "free")) -> glht.out  
emm.interaction %>%   
  as.data.frame() %>%
  dplyr::select(-SE) %>%
  mutate(p.adj = glht.out$test$pvalues) %>%
  mutate(p.adj = sapply(p.adj,max,.Machine$double.eps)) %>%
  mutate(maxBF = 1/pCalibrate(p.adj,"exploratory")) %>%
  mutate_at("maxBF", formatC, format='g',digits = 3) %>%
  mutate(p.value = afex::round_ps_apa(p.value)) %>%
  mutate(p.adj = afex::round_ps_apa(p.adj)) %>%
  knitr::kable(caption = sprintf("**Hypothesis testing on interaction parameters (Dunnett's step-down p-value adjustment): %s**",resp), digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

}
# Replot data with 95% confidence intervals
emf(sprintf("../img/%s_%s.emf","n2b_mutants",resp), width=4.5, height=3.5)
emm %>%
    as.data.frame() %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) -> emm_df
p1 <- Data %>%
    mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
           grouping=interaction(pair, mutation)) %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
    ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) + 
    geom_blank() +
    geom_line(aes(mutation_jittered), alpha=0.3, color="grey", size=0.75) +
    geom_point(aes(mutation_jittered), alpha=0.6, shape = 16, size=1.25) +
    scale_color_manual(values=c("grey","#00BA38")) +
    scale_fill_manual(values=c("grey","#00BA38")) +
    geom_crossbar(data = emm_df, 
                    aes(x=mutation_transfection, y=response, ymin=`lower.CL`, ymax=`upper.CL`, fill=transfection), 
                    color="black", alpha=0.5, size=0.5, fatten=1, width=0.3, inherit.aes=FALSE) + 
    ylab(resp) +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.title = element_blank(),
          legend.position = c(0.5, 1.06),
          legend.direction = "horizontal",
          text = element_text(size=14))
emm.transfection %>%
    confint() %>%
    as.data.frame() -> emm.transfection_df
p2 <- Data %>% 
    pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>% 
    mutate(ratio = `+`/`-`) %>%
    ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
    geom_sina(alpha=0.6, shape=16, size=1.25, maxwidth=0.5) + 
    geom_crossbar(data = emm.transfection_df, 
                           aes(x=mutation, y=ratio, ymin=`lower.CL`, ymax=`upper.CL`, fill=mutation), 
                    color="black", alpha=0.5, size=0.5, fatten=1, width=0.8, inherit.aes=FALSE) +
    scale_colour_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
    scale_fill_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
    ylab("ratio") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1), axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.position = "none",
          text=element_text(size=14))
grid.arrange(p1, p2, layout_matrix=rbind(c(1,2)), top=sprintf("Summary plots of the data with 95%% confidence intervals: %s\n",resp))
dev.off() #turn off device and finalize file


}

Analysis of peaknmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peaknmda
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: peaknmda
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: peaknmda
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: peaknmda
transfection contrast
- -0.5
+ 0.5
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peaknmda
Source F Df Df.res Pr(>F) BF
mutation 2.91 4 16.22 .055 2.01
transfection 49.75 1 90.00 <.001 5.87e+07
mutation:transfection 0.21 4 90.00 .933 0.052
mutationWT_vs_Mutants:transfection1 0.38 1 90.00 .540
mutationLOF_vs_GOF:transfection1 0.40 1 90.00 .530
mutationLOF:transfection1 0.07 1 90.00 .796
mutationGOF:transfection1 0.04 1 90.00 .845
Intraclass correlation coefficients for random effects: peaknmda
Group ICC N
pair:(slice:animal) 0.160 95
slice:animal 0.267 62
animal 0.363 22
residual 0.209 190
Estimated marginal means with 95% confidence intervals: peaknmda
mutation transfection df response n lower.CL upper.CL
WT - 16.59 162.39 19 86.29 305.61
R540H - 17.91 93.71 26 54.83 160.16
R696H - 18.08 43.03 17 22.63 81.83
C456Y - 20.05 108.92 18 60.44 196.29
C461F - 16.34 71.70 15 34.66 148.34
WT + 16.59 117.93 19 62.66 221.95
R540H + 17.91 66.16 26 38.71 113.07
R696H + 18.08 29.42 17 15.47 55.94
C456Y + 20.05 71.72 18 39.80 129.24
C461F + 16.34 45.01 15 21.76 93.12
[1] “Overall average of peaknmda for untransfected neurons: 87.45”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peaknmda
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 90 0.73 0.57 0.92
(+) / (-) R540H 90 0.71 0.58 0.87
(+) / (-) R696H 90 0.68 0.53 0.88
(+) / (-) C456Y 90 0.66 0.51 0.84
(+) / (-) C461F 90 0.63 0.48 0.82
95% confidence intervals for contrasts: peaknmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 90 0.97 0.71 1.33
R696H / WT (+) / (-) 90 0.94 0.66 1.33
C456Y / WT (+) / (-) 90 0.91 0.64 1.28
C461F / WT (+) / (-) 90 0.86 0.60 1.24
Standardized effect sizes (r) for contrasts: peaknmda
mutation transfection n r 95% CI
R540H / WT (+) / (-) 95 -0.02 [-0.22, 0.18]
R696H / WT (+) / (-) 95 -0.04 [-0.24, 0.17]
C456Y / WT (+) / (-) 95 -0.06 [-0.26, 0.14]
C461F / WT (+) / (-) 95 -0.08 [-0.28, 0.12]

Analysis of decaynmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decaynmda
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: decaynmda
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: decaynmda
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: decaynmda
transfection contrast
- -0.5
+ 0.5
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decaynmda
Source F Df Df.res Pr(>F) BF
mutation 2.67 4 15.78 .071 0.642
transfection 48.45 1 90.00 <.001 7.96e+06
mutation:transfection 8.85 4 90.00 <.001 1.52e+05
mutationWT_vs_Mutants:transfection1 19.20 1 90.00 <.001
mutationLOF_vs_GOF:transfection1 17.17 1 90.00 <.001
mutationLOF:transfection1 0.76 1 90.00 .386
mutationGOF:transfection1 0.24 1 90.00 .626
Intraclass correlation coefficients for random effects: decaynmda
Group ICC N
pair:(slice:animal) 0.000 95
slice:animal 0.000 62
animal 0.187 22
residual 0.813 190
Estimated marginal means with 95% confidence intervals: decaynmda
mutation transfection df response n lower.CL upper.CL
WT - 24.97 71.51 19 54.27 94.23
R540H - 27.26 72.21 26 56.81 91.78
R696H - 27.90 56.05 17 42.20 74.44
C456Y - 32.47 71.19 18 54.71 92.62
C461F - 23.62 99.03 15 72.36 135.54
WT + 24.97 79.02 19 59.96 104.12
R540H + 27.26 57.93 26 45.57 73.63
R696H + 27.90 41.26 17 31.06 54.80
C456Y + 32.47 34.47 18 26.49 44.85
C461F + 23.62 40.38 15 29.50 55.27
[1] “Overall average of decaynmda for untransfected neurons: 72.77”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decaynmda
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 90 1.10 0.85 1.43
(+) / (-) R540H 90 0.80 0.64 1.00
(+) / (-) R696H 90 0.74 0.56 0.97
(+) / (-) C456Y 90 0.48 0.37 0.63
(+) / (-) C461F 90 0.41 0.31 0.54
95% confidence intervals for contrasts: decaynmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 90 0.73 0.52 1.02
R696H / WT (+) / (-) 90 0.67 0.46 0.97
C456Y / WT (+) / (-) 90 0.44 0.30 0.63
C461F / WT (+) / (-) 90 0.37 0.25 0.54
Standardized effect sizes (r) for contrasts: decaynmda
mutation transfection n r 95% CI
R540H / WT (+) / (-) 95 -0.19 [-0.38, 0.01]
R696H / WT (+) / (-) 95 -0.22 [-0.41, -0.02]
C456Y / WT (+) / (-) 95 -0.42 [-0.58, -0.24]
C461F / WT (+) / (-) 95 -0.47 [-0.62, -0.30]

Analysis of dt50nmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50nmda
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: dt50nmda
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: dt50nmda
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: dt50nmda
transfection contrast
- -0.5
+ 0.5
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50nmda
Source F Df Df.res Pr(>F) BF
mutation 4.62 4 16.09 .011 3.42
transfection 56.58 1 90.00 <.001 2.03e+07
mutation:transfection 10.45 4 90.00 <.001 1.19e+06
mutationWT_vs_Mutants:transfection1 21.18 1 90.00 <.001
mutationLOF_vs_GOF:transfection1 19.67 1 90.00 <.001
mutationLOF:transfection1 0.89 1 90.00 .349
mutationGOF:transfection1 1.72 1 90.00 .193
Intraclass correlation coefficients for random effects: dt50nmda
Group ICC N
pair:(slice:animal) 0.000 95
slice:animal 0.029 62
animal 0.255 22
residual 0.716 190
Estimated marginal means with 95% confidence intervals: dt50nmda
mutation transfection df response n lower.CL upper.CL
WT - 22.31 34.67 19 27.75 43.31
R540H - 24.89 32.48 26 26.82 39.33
R696H - 24.61 23.44 17 18.67 29.42
C456Y - 28.03 35.05 18 28.42 43.22
C461F - 21.32 35.98 15 27.90 46.38
WT + 22.31 36.98 19 29.60 46.20
R540H + 24.89 28.87 26 23.84 34.96
R696H + 24.61 17.73 17 14.12 22.26
C456Y + 28.03 20.35 18 16.50 25.10
C461F + 21.32 18.35 15 14.23 23.65
[1] “Overall average of dt50nmda for untransfected neurons: 31.95”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50nmda
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 90 1.07 0.89 1.28
(+) / (-) R540H 90 0.89 0.76 1.04
(+) / (-) R696H 90 0.76 0.63 0.92
(+) / (-) C456Y 90 0.58 0.48 0.70
(+) / (-) C461F 90 0.51 0.42 0.62
95% confidence intervals for contrasts: dt50nmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 90 0.83 0.66 1.06
R696H / WT (+) / (-) 90 0.71 0.55 0.92
C456Y / WT (+) / (-) 90 0.54 0.42 0.70
C461F / WT (+) / (-) 90 0.48 0.36 0.63
Standardized effect sizes (r) for contrasts: dt50nmda
mutation transfection n r 95% CI
R540H / WT (+) / (-) 95 -0.16 [-0.35, 0.04]
R696H / WT (+) / (-) 95 -0.26 [-0.44, -0.07]
C456Y / WT (+) / (-) 95 -0.44 [-0.59, -0.26]
C461F / WT (+) / (-) 95 -0.50 [-0.63, -0.33]

Analysis of chargenmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargenmda
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: chargenmda
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: chargenmda
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: chargenmda
transfection contrast
- -0.5
+ 0.5
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargenmda
Source F Df Df.res Pr(>F) BF
mutation 3.00 4 16.28 .050 2.09
transfection 57.13 1 90.00 <.001 1.09e+08
mutation:transfection 3.63 4 90.00 .009 5.16
mutationWT_vs_Mutants:transfection1 4.81 1 90.00 .031
mutationLOF_vs_GOF:transfection1 10.02 1 90.00 .002
mutationLOF:transfection1 0.75 1 90.00 .389
mutationGOF:transfection1 0.13 1 90.00 .716
Intraclass correlation coefficients for random effects: chargenmda
Group ICC N
pair:(slice:animal) 0.094 95
slice:animal 0.219 62
animal 0.362 22
residual 0.325 190
Estimated marginal means with 95% confidence intervals: chargenmda
mutation transfection df response n lower.CL upper.CL
WT - 17.52 16.04 19 7.85 32.76
R540H - 19.05 8.50 26 4.64 15.56
R696H - 19.06 3.48 17 1.68 7.19
C456Y - 21.16 11.34 18 5.83 22.06
C461F - 17.19 8.66 15 3.81 19.67
WT + 17.52 12.47 19 6.10 25.47
R540H + 19.05 5.48 26 2.99 10.03
R696H + 19.06 2.44 17 1.18 5.04
C456Y + 21.16 4.91 18 2.52 9.54
C461F + 17.19 2.99 15 1.32 6.79
[1] “Overall average of chargenmda for untransfected neurons: 8.58”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargenmda
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 90 0.78 0.55 1.09
(+) / (-) R540H 90 0.64 0.48 0.86
(+) / (-) R696H 90 0.70 0.49 1.01
(+) / (-) C456Y 90 0.43 0.30 0.61
(+) / (-) C461F 90 0.35 0.24 0.51
95% confidence intervals for contrasts: chargenmda
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 90 0.83 0.53 1.30
R696H / WT (+) / (-) 90 0.90 0.55 1.48
C456Y / WT (+) / (-) 90 0.56 0.34 0.91
C461F / WT (+) / (-) 90 0.44 0.27 0.74
Standardized effect sizes (r) for contrasts: chargenmda
mutation transfection n r 95% CI
R540H / WT (+) / (-) 95 -0.09 [-0.28, 0.12]
R696H / WT (+) / (-) 95 -0.04 [-0.24, 0.16]
C456Y / WT (+) / (-) 95 -0.24 [-0.42, -0.04]
C461F / WT (+) / (-) 95 -0.31 [-0.49, -0.12]

Analysis of peakampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peakampa
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: peakampa
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: peakampa
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: peakampa
transfection contrast
- -0.5
+ 0.5
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peakampa
Source F Df Df.res Pr(>F) BF
mutation 9.08 4 16.25 <.001 59.3
transfection 0.77 1 90.00 .383 0.194
mutation:transfection 1.17 4 90.00 .328 0.197
mutationWT_vs_Mutants:transfection1 0.18 1 90.00 .677
mutationLOF_vs_GOF:transfection1 1.22 1 90.00 .273
mutationLOF:transfection1 3.41 1 90.00 .068
mutationGOF:transfection1 0.14 1 90.00 .706
Intraclass correlation coefficients for random effects: peakampa
Group ICC N
pair:(slice:animal) 0.087 95
slice:animal 0.180 62
animal 0.341 22
residual 0.392 190
Estimated marginal means with 95% confidence intervals: peakampa
mutation transfection df response n lower.CL upper.CL
WT - 18.09 5.72 19 3.17 10.31
R540H - 19.79 18.10 26 10.97 29.86
R696H - 19.77 51.28 17 28.14 93.44
C456Y - 22.04 20.59 18 11.88 35.69
C461F - 17.68 20.78 15 10.56 40.88
WT + 18.09 5.05 19 2.80 9.12
R540H + 19.79 18.10 26 10.97 29.87
R696H + 19.77 55.67 17 30.55 101.43
C456Y + 22.04 22.43 18 12.94 38.89
C461F + 17.68 14.46 15 7.35 28.45
[1] “Overall average of peakampa for untransfected neurons: 18.67”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peakampa
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 90 0.88 0.64 1.21
(+) / (-) R540H 90 1.00 0.76 1.31
(+) / (-) R696H 90 1.09 0.78 1.52
(+) / (-) C456Y 90 1.09 0.79 1.51
(+) / (-) C461F 90 0.70 0.49 0.99
95% confidence intervals for contrasts: peakampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 90 1.13 0.75 1.72
R696H / WT (+) / (-) 90 1.23 0.78 1.95
C456Y / WT (+) / (-) 90 1.23 0.78 1.94
C461F / WT (+) / (-) 90 0.79 0.49 1.27
Standardized effect sizes (r) for contrasts: peakampa
mutation transfection n r 95% CI
R540H / WT (+) / (-) 95 0.06 [-0.14, 0.26]
R696H / WT (+) / (-) 95 0.09 [-0.11, 0.29]
C456Y / WT (+) / (-) 95 0.10 [-0.11, 0.29]
C461F / WT (+) / (-) 95 -0.10 [-0.30, 0.10]

Analysis of decayampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decayampa
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: decayampa
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: decayampa
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: decayampa
transfection contrast
- -0.5
+ 0.5
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decayampa
Source F Df Df.res Pr(>F) BF
mutation 2.33 4 16.58 .099 1.37
transfection 1.42 1 90.00 .237 0.37
mutation:transfection 0.94 4 90.00 .446 0.16
mutationWT_vs_Mutants:transfection1 0.00 1 90.00 .956
mutationLOF_vs_GOF:transfection1 0.00 1 90.00 .990
mutationLOF:transfection1 3.73 1 90.00 .057
mutationGOF:transfection1 0.00 1 90.00 .994
Intraclass correlation coefficients for random effects: decayampa
Group ICC N
pair:(slice:animal) 0.000 95
slice:animal 0.210 62
animal 0.501 22
residual 0.289 190
Estimated marginal means with 95% confidence intervals: decayampa
mutation transfection df response n lower.CL upper.CL
WT - 17.51 8.33 19 5.05 13.73
R540H - 18.88 6.97 26 4.59 10.58
R696H - 18.52 3.44 17 2.08 5.70
C456Y - 20.01 7.52 18 4.75 11.91
C461F - 17.32 4.56 15 2.57 8.11
WT + 17.51 7.92 19 4.80 13.05
R540H + 18.88 6.58 26 4.33 9.99
R696H + 18.52 3.24 17 1.96 5.38
C456Y + 20.01 6.10 18 3.86 9.66
C461F + 17.32 5.02 15 2.82 8.92
[1] “Overall average of decayampa for untransfected neurons: 5.85”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decayampa
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 90 0.95 0.77 1.17
(+) / (-) R540H 90 0.94 0.79 1.13
(+) / (-) R696H 90 0.94 0.76 1.17
(+) / (-) C456Y 90 0.81 0.66 1.00
(+) / (-) C461F 90 1.10 0.87 1.39
95% confidence intervals for contrasts: decayampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 90 0.99 0.76 1.30
R696H / WT (+) / (-) 90 0.99 0.74 1.34
C456Y / WT (+) / (-) 90 0.85 0.64 1.15
C461F / WT (+) / (-) 90 1.16 0.85 1.58
Standardized effect sizes (r) for contrasts: decayampa
mutation transfection n r 95% CI
R540H / WT (+) / (-) 95 -0.01 [-0.21, 0.20]
R696H / WT (+) / (-) 95 -0.01 [-0.21, 0.20]
C456Y / WT (+) / (-) 95 -0.11 [-0.31, 0.09]
C461F / WT (+) / (-) 95 0.10 [-0.11, 0.29]

Analysis of dt50ampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50ampa
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: dt50ampa
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: dt50ampa
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: dt50ampa
transfection contrast
- -0.5
+ 0.5
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50ampa
Source F Df Df.res Pr(>F) BF
mutation 3.00 4 16.11 .050 1.58
transfection 2.96 1 90.00 .089 0.694
mutation:transfection 0.31 4 90.00 .871 0.0663
mutationWT_vs_Mutants:transfection1 0.87 1 90.00 .354
mutationLOF_vs_GOF:transfection1 0.05 1 90.00 .830
mutationLOF:transfection1 0.04 1 90.00 .839
mutationGOF:transfection1 0.31 1 90.00 .579
Intraclass correlation coefficients for random effects: dt50ampa
Group ICC N
pair:(slice:animal) 0.100 95
slice:animal 0.012 62
animal 0.286 22
residual 0.602 190
Estimated marginal means with 95% confidence intervals: dt50ampa
mutation transfection df response n lower.CL upper.CL
WT - 20.62 4.28 19 3.21 5.71
R540H - 23.15 3.87 26 3.01 4.96
R696H - 22.52 2.60 17 1.93 3.49
C456Y - 25.36 3.96 18 3.02 5.20
C461F - 19.69 3.11 15 2.24 4.33
WT + 20.62 4.30 19 3.22 5.75
R540H + 23.15 3.67 26 2.86 4.70
R696H + 22.52 2.28 17 1.70 3.06
C456Y + 25.36 3.48 18 2.65 4.56
C461F + 19.69 2.82 15 2.03 3.92
[1] “Overall average of dt50ampa for untransfected neurons: 3.51”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50ampa
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 90 1.01 0.82 1.24
(+) / (-) R540H 90 0.95 0.79 1.13
(+) / (-) R696H 90 0.88 0.70 1.09
(+) / (-) C456Y 90 0.88 0.71 1.09
(+) / (-) C461F 90 0.91 0.72 1.14
95% confidence intervals for contrasts: dt50ampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 90 0.94 0.72 1.24
R696H / WT (+) / (-) 90 0.87 0.64 1.18
C456Y / WT (+) / (-) 90 0.87 0.65 1.17
C461F / WT (+) / (-) 90 0.90 0.66 1.23
Standardized effect sizes (r) for contrasts: dt50ampa
mutation transfection n r 95% CI
R540H / WT (+) / (-) 95 -0.04 [-0.24, 0.16]
R696H / WT (+) / (-) 95 -0.09 [-0.29, 0.11]
C456Y / WT (+) / (-) 95 -0.10 [-0.29, 0.11]
C461F / WT (+) / (-) 95 -0.07 [-0.27, 0.13]

Analysis of chargeampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargeampa
WT_vs_Mutants LOF_vs_GOF LOF GOF
WT_vs_Mutants 1 0 0 0
LOF_vs_GOF 0 1 0 0
LOF 0 0 1 0
GOF 0 0 0 1
Sum of each orthogonal contrast should be zero: chargeampa
sum
WT_vs_Mutants 0
LOF_vs_GOF 0
LOF 0
GOF 0
Matrix of contrasts on mutation: chargeampa
mutation WT_vs_Mutants LOF_vs_GOF LOF GOF
WT -0.8 0.0 0.0 0.0
R540H 0.2 0.5 0.0 -0.5
R696H 0.2 0.5 0.0 0.5
C456Y 0.2 -0.5 -0.5 0.0
C461F 0.2 -0.5 0.5 0.0
Matrix of contrasts on transfection: chargeampa
transfection contrast
- -0.5
+ 0.5
## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargeampa
Source F Df Df.res Pr(>F) BF
mutation 5.90 4 15.68 .004 10.7
transfection 1.68 1 90.00 .198 0.245
mutation:transfection 1.84 4 90.00 .128 0.482
mutationWT_vs_Mutants:transfection1 1.88 1 90.00 .174
mutationLOF_vs_GOF:transfection1 2.70 1 90.00 .104
mutationLOF:transfection1 3.38 1 90.00 .069
mutationGOF:transfection1 0.05 1 90.00 .829
Intraclass correlation coefficients for random effects: chargeampa
Group ICC N
pair:(slice:animal) 0.177 95
slice:animal 0.139 62
animal 0.195 22
residual 0.489 190
Estimated marginal means with 95% confidence intervals: chargeampa
mutation transfection df response n lower.CL upper.CL
WT - 19.19 0.29 19 0.18 0.48
R540H - 20.31 0.83 26 0.54 1.28
R696H - 21.86 1.27 17 0.76 2.11
C456Y - 25.47 1.03 18 0.64 1.65
C461F - 18.59 0.79 15 0.45 1.39
WT + 19.19 0.33 19 0.20 0.54
R540H + 20.31 0.85 26 0.55 1.30
R696H + 21.86 1.22 17 0.73 2.04
C456Y + 25.47 0.97 18 0.60 1.55
C461F + 18.59 0.46 15 0.26 0.81
[1] “Overall average of chargeampa for untransfected neurons: 0.76”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargeampa
contrast mutation df ratio lower.CL upper.CL
(+) / (-) WT 90 1.12 0.79 1.57
(+) / (-) R540H 90 1.02 0.76 1.36
(+) / (-) R696H 90 0.97 0.67 1.38
(+) / (-) C456Y 90 0.94 0.67 1.34
(+) / (-) C461F 90 0.58 0.40 0.86
95% confidence intervals for contrasts: chargeampa
mutation_trt.vs.ctrl transfection_trt.vs.ctrl df ratio lower.CL upper.CL
R540H / WT (+) / (-) 90 0.91 0.58 1.42
R696H / WT (+) / (-) 90 0.86 0.53 1.42
C456Y / WT (+) / (-) 90 0.85 0.52 1.38
C461F / WT (+) / (-) 90 0.52 0.31 0.87
Standardized effect sizes (r) for contrasts: chargeampa
mutation transfection n r 95% CI
R540H / WT (+) / (-) 95 -0.04 [-0.24, 0.16]
R696H / WT (+) / (-) 95 -0.06 [-0.26, 0.14]
C456Y / WT (+) / (-) 95 -0.07 [-0.27, 0.13]
C461F / WT (+) / (-) 95 -0.26 [-0.43, -0.06]